A 3-D-Point-Cloud System for Human-Pose Estimation

This paper focuses on human-pose estimation using a stationary depth sensor. The main challenge concerns reducing the feature ambiguity and modeling human poses in high-dimensional human-pose space because of the curse of dimensionality. We propose a 3-D-point-cloud system that captures the geometric properties (orientation and shape) of the 3-D point cloud of a human to reduce the feature ambiguity, and use the result from action classification to discover low-dimensional manifolds in human-pose space in estimating the underlying probability distribution of human poses. In the proposed system, a 3-D-point-cloud feature called viewpoint and shape feature histogram (VISH) is proposed to extract the 3-D points from a human and arrange them into a tree structure that preserves the global and local properties of the 3-D points. A nonparametric action-mixture model (AMM) is then proposed to model human poses using low-dimensional manifolds based on the concept of distributed representation. Since human poses estimated using the proposed AMM are in discrete space, a kinematic model is added in the last stage of the proposed system to model the spatial relationship of body parts in continuous space to reduce the quantization error in the AMM. The proposed system has been trained and evaluated on a benchmark dataset. Computer-simulation results showed that the overall error and standard deviation of the proposed 3-D-point-cloud system were reduced compared with some existing approaches without action classification.